Dai un'occhiata al repository del libro di ricette di Gemma per esempi di generazione e ottimizzazione. Scopri di più

Questa pagina è stata tradotta dall'API Cloud Translation.

Inferenza con Gemma utilizzando JAX e Lino

Visualizza su ai.google.dev

Esegui in Google Colab

Apri in Vertex AI

Visualizza il codice sorgente su GitHub

Panoramica

Gemma è una famiglia di modelli linguistici di grandi dimensioni aperti, leggeri e all'avanguardia, basati sulla ricerca e sulla tecnologia Gemini di Google DeepMind. Questo tutorial dimostra come eseguire campionamenti/inferenze di base con il modello Gemma 2B Instruct utilizzando la libreria gemma di Google DeepMind scritta con JAX (una libreria di calcolo numerico ad alte prestazioni), Flax (la libreria di rete neurale basata su JAX), Orbax (una libreria basata su JAX per utilità di addestramento come la libreria di checkpointing) e SentencePiece Sebbene Flax non sia utilizzato direttamente in questo blocco note, Flax è stato utilizzato per creare Gemma.

Questo blocco note può essere eseguito su Google Colab con GPU T4 senza costi (vai a Modifica > Impostazioni blocco note > nella sezione Acceleratore hardware seleziona GPU T4).

Configurazione

1. Configura l'accesso a Kaggle per Gemma

Per completare questo tutorial, devi prima seguire le istruzioni di configurazione nella configurazione di Gemma, che mostrano come fare:

Accedi a Gemma su kaggle.com.
Seleziona un runtime Colab con risorse sufficienti per eseguire il modello Gemma.
Genera e configura un nome utente e una chiave API Kaggle.

Dopo aver completato la configurazione di Gemma, passa alla sezione successiva, in cui imposterai le variabili di ambiente per il tuo ambiente Colab.

2. Imposta le variabili di ambiente

Imposta le variabili di ambiente per KAGGLE_USERNAME e KAGGLE_KEY. Quando viene visualizzata la richiesta "Vuoi concedere l'accesso?" messaggi, accetti di fornire l'accesso al secret.

import os
from google.colab import userdata # `userdata` is a Colab API.

os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

3. Installa la libreria `gemma`

Questo blocco note è incentrato sull'utilizzo di una GPU Colab senza costi. Per attivare l'accelerazione hardware, fai clic su Modifica > Impostazioni blocco note > Seleziona GPU T4 > Salva.

Successivamente, devi installare la libreria Google DeepMind gemma da github.com/google-deepmind/gemma. Se ricevi un errore relativo al " resolver di dipendenze di pip", in genere puoi ignorarlo.

pip install -q git+https://github.com/google-deepmind/gemma.git

Carica e prepara il modello Gemma

Carica il modello Gemma con kagglehub.model_download, che accetta tre argomenti:

handle: l'handle del modello di Kaggle
path: (stringa facoltativa) il percorso locale
force_download: (booleano facoltativo) forza a scaricare di nuovo il modello

di Gemini Advanced.

GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:"string"}

import kagglehub

GEMMA_PATH = kagglehub.model_download(f'google/gemma-2/flax/{GEMMA_VARIANT}')

Downloading 11 files:   0%|          | 0/11 [00:00<?, ?it/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/manifest.ocdbt...
100%|██████████| 180/180 [00:00<00:00, 101kB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/d/b5a4695f4be0a2f41ec1e25616ebd7e7...
100%|██████████| 2.66k/2.66k [00:00<00:00, 5.36MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/descriptor/descriptor.pbtxt...
100%|██████████| 45.0/45.0 [00:00<00:00, 90.0kB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/_METADATA...
100%|██████████| 55.3k/55.3k [00:00<00:00, 29.5MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/_CHECKPOINT_METADATA...
100%|██████████| 92.0/92.0 [00:00<00:00, 234kB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/bf69258061ae5f35eb7a5669fe6877d4...
0%|          | 0.00/2.12G [00:00<?, ?B/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/fc20151969d7ca91ea9d8275bda0e219...
100%|██████████| 2.64k/2.64k [00:00<00:00, 5.58MB/s]

  0%|          | 2.00M/2.12G [00:00<01:48, 20.8MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/ocdbt.process_0/d/834bb4bf1e3854eb09f6208c95c071b2...
0%|          | 0.00/1.70G [00:00<?, ?B/s]
  0%|          | 9.00M/2.12G [00:00<00:46, 48.2MB/s]

  0%|          | 3.00M/1.70G [00:00<01:06, 27.6MB/s]
  1%|          | 14.0M/2.12G [00:00<00:46, 48.6MB/s]

  1%|          | 9.00M/1.70G [00:00<00:40, 44.5MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/manifest.ocdbt...
100%|██████████| 118/118 [00:00<00:00, 303kB/s]

  1%|          | 21.0M/2.12G [00:00<00:41, 53.7MB/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/gemma2-2b-it/checkpoint...
0%|          | 0.00/22.5k [00:00<?, ?B/s]
Downloading from https://www.kaggle.com/api/v1/models/google/gemma-2-2b/flax/gemma2-2b-it/1/download/tokenizer.model...
100%|██████████| 22.5k/22.5k [00:00<00:00, 24.7MB/s]


  1%|          | 17.0M/1.70G [00:00<00:36, 49.5MB/s]


  0%|          | 0.00/4.04M [00:00<?, ?B/s]
100%|██████████| 4.04M/4.04M [00:00<00:00, 64.6MB/s]


  1%|▏         | 24.0M/1.70G [00:00<00:34, 52.7MB/s]
  2%|▏         | 40.0M/2.12G [00:00<00:34, 64.6MB/s]

  2%|▏         | 33.0M/1.70G [00:00<00:27, 64.4MB/s]
  2%|▏         | 49.0M/2.12G [00:00<00:34, 64.8MB/s]

  3%|▎         | 47.0M/1.70G [00:00<00:20, 86.9MB/s]
  3%|▎         | 59.0M/2.12G [00:00<00:29, 74.4MB/s]

  3%|▎         | 56.0M/1.70G [00:00<00:24, 73.1MB/s]
  3%|▎         | 67.0M/2.12G [00:01<00:31, 70.1MB/s]

  4%|▎         | 64.0M/1.70G [00:01<00:25, 69.4MB/s]
  3%|▎         | 74.0M/2.12G [00:01<00:32, 67.4MB/s]

  4%|▍         | 73.0M/1.70G [00:01<00:23, 75.7MB/s]
  4%|▍         | 84.0M/2.12G [00:01<00:28, 75.5MB/s]

  5%|▍         | 81.0M/1.70G [00:01<00:22, 77.7MB/s]

  5%|▌         | 95.0M/1.70G [00:01<00:17, 96.5MB/s]
  4%|▍         | 92.0M/2.12G [00:01<00:38, 56.7MB/s]

  6%|▌         | 106M/1.70G [00:01<00:17, 101MB/s]  
  5%|▍         | 102M/2.12G [00:01<00:32, 67.2MB/s] 

  7%|▋         | 117M/1.70G [00:01<00:16, 102MB/s]
  5%|▌         | 110M/2.12G [00:01<00:30, 70.5MB/s]

  7%|▋         | 128M/1.70G [00:01<00:16, 105MB/s]
  5%|▌         | 119M/2.12G [00:01<00:28, 75.6MB/s]

  8%|▊         | 142M/1.70G [00:01<00:14, 117MB/s]
  6%|▌         | 129M/2.12G [00:02<00:30, 70.1MB/s]

  9%|▉         | 154M/1.70G [00:01<00:17, 92.5MB/s]
  6%|▋         | 138M/2.12G [00:02<00:28, 73.9MB/s]

  9%|▉         | 164M/1.70G [00:02<00:18, 87.7MB/s]
  7%|▋         | 146M/2.12G [00:02<00:30, 70.2MB/s]

 10%|▉         | 173M/1.70G [00:02<00:20, 81.7MB/s]
  7%|▋         | 153M/2.12G [00:02<00:33, 63.0MB/s]

 10%|█         | 182M/1.70G [00:02<00:19, 82.8MB/s]
  8%|▊         | 164M/2.12G [00:02<00:27, 75.3MB/s]

 11%|█         | 195M/1.70G [00:02<00:17, 90.8MB/s]
  8%|▊         | 174M/2.12G [00:02<00:25, 82.0MB/s]

 12%|█▏        | 207M/1.70G [00:02<00:16, 99.0MB/s]
  9%|▊         | 186M/2.12G [00:02<00:22, 92.9MB/s]

 13%|█▎        | 218M/1.70G [00:02<00:16, 99.7MB/s]
  9%|▉         | 196M/2.12G [00:02<00:22, 92.4MB/s]

 13%|█▎        | 229M/1.70G [00:02<00:15, 103MB/s] 
 10%|▉         | 206M/2.12G [00:02<00:22, 92.1MB/s]

 14%|█▎        | 239M/1.70G [00:02<00:15, 99.4MB/s]
 10%|▉         | 215M/2.12G [00:03<00:22, 91.3MB/s]

 14%|█▍        | 250M/1.70G [00:03<00:15, 101MB/s] 
 10%|█         | 226M/2.12G [00:03<00:21, 96.5MB/s]

 15%|█▌        | 263M/1.70G [00:03<00:14, 108MB/s]
 11%|█         | 238M/2.12G [00:03<00:19, 105MB/s] 
 11%|█▏        | 249M/2.12G [00:03<00:19, 103MB/s]

 16%|█▌        | 274M/1.70G [00:03<00:16, 91.6MB/s]
 12%|█▏        | 259M/2.12G [00:03<00:21, 93.3MB/s]

 16%|█▋        | 284M/1.70G [00:03<00:20, 76.0MB/s]
 12%|█▏        | 269M/2.12G [00:03<00:21, 94.3MB/s]

 17%|█▋        | 295M/1.70G [00:03<00:18, 84.0MB/s]
 13%|█▎        | 279M/2.12G [00:03<00:20, 94.2MB/s]

 17%|█▋        | 304M/1.70G [00:03<00:17, 84.4MB/s]
 13%|█▎        | 289M/2.12G [00:03<00:20, 94.9MB/s]

 18%|█▊        | 313M/1.70G [00:03<00:18, 81.9MB/s]
 14%|█▍        | 299M/2.12G [00:03<00:21, 91.4MB/s]
 14%|█▍        | 308M/2.12G [00:04<00:21, 89.4MB/s]

 18%|█▊        | 322M/1.70G [00:03<00:20, 73.5MB/s]

 19%|█▉        | 330M/1.70G [00:04<00:19, 74.9MB/s]
 15%|█▍        | 317M/2.12G [00:04<00:23, 81.1MB/s]
 15%|█▌        | 326M/2.12G [00:04<00:23, 83.6MB/s]

 19%|█▉        | 338M/1.70G [00:04<00:20, 72.0MB/s]

 20%|█▉        | 346M/1.70G [00:04<00:19, 74.6MB/s]
 15%|█▌        | 335M/2.12G [00:04<00:24, 79.2MB/s]

 20%|██        | 354M/1.70G [00:04<00:19, 75.0MB/s]
 16%|█▌        | 344M/2.12G [00:04<00:23, 81.4MB/s]
 16%|█▋        | 352M/2.12G [00:04<00:28, 67.3MB/s]

 21%|██        | 362M/1.70G [00:04<00:26, 54.3MB/s]
 17%|█▋        | 359M/2.12G [00:04<00:31, 59.6MB/s]

 21%|██        | 369M/1.70G [00:04<00:26, 53.4MB/s]
 17%|█▋        | 366M/2.12G [00:05<00:31, 59.0MB/s]

 22%|██▏       | 375M/1.70G [00:04<00:26, 54.9MB/s]
 17%|█▋        | 372M/2.12G [00:05<00:31, 59.2MB/s]

 22%|██▏       | 381M/1.70G [00:05<00:25, 56.2MB/s]
 17%|█▋        | 379M/2.12G [00:05<00:30, 62.3MB/s]

 22%|██▏       | 388M/1.70G [00:05<00:24, 56.8MB/s]
 18%|█▊        | 386M/2.12G [00:05<00:29, 63.8MB/s]

 23%|██▎       | 395M/1.70G [00:05<00:23, 60.2MB/s]
 18%|█▊        | 394M/2.12G [00:05<00:27, 68.5MB/s]

 23%|██▎       | 402M/1.70G [00:05<00:22, 62.7MB/s]
 19%|█▊        | 401M/2.12G [00:05<00:27, 66.3MB/s]

 23%|██▎       | 409M/1.70G [00:05<00:21, 64.4MB/s]
 19%|█▉        | 408M/2.12G [00:05<00:28, 65.4MB/s]

 24%|██▍       | 416M/1.70G [00:05<00:21, 65.4MB/s]

 24%|██▍       | 423M/1.70G [00:05<00:26, 51.4MB/s]
 19%|█▉        | 415M/2.12G [00:07<03:02, 10.1MB/s]

 25%|██▍       | 429M/1.70G [00:08<02:56, 7.79MB/s]
 19%|█▉        | 420M/2.12G [00:08<03:17, 9.28MB/s]

 25%|██▌       | 439M/1.70G [00:08<01:52, 12.2MB/s]
 20%|█▉        | 432M/2.12G [00:08<01:56, 15.7MB/s]

 26%|██▌       | 447M/1.70G [00:08<01:22, 16.5MB/s]
 20%|██        | 441M/2.12G [00:08<01:25, 21.1MB/s]

 26%|██▌       | 454M/1.70G [00:08<01:05, 20.5MB/s]
 21%|██        | 448M/2.12G [00:09<01:14, 24.0MB/s]

 26%|██▋       | 460M/1.70G [00:08<00:54, 24.4MB/s]

 27%|██▋       | 468M/1.70G [00:09<00:42, 31.6MB/s]
 21%|██        | 454M/2.12G [00:09<01:07, 26.4MB/s]
 21%|██▏       | 464M/2.12G [00:09<00:49, 36.4MB/s]

 27%|██▋       | 476M/1.70G [00:09<00:38, 34.4MB/s]

 28%|██▊       | 487M/1.70G [00:09<00:28, 47.0MB/s]
 22%|██▏       | 471M/2.12G [00:09<00:53, 33.2MB/s]

 28%|██▊       | 495M/1.70G [00:09<00:28, 46.0MB/s]
 22%|██▏       | 477M/2.12G [00:09<00:49, 36.0MB/s]

 29%|██▉       | 502M/1.70G [00:09<00:27, 47.6MB/s]

 29%|██▉       | 510M/1.70G [00:09<00:23, 54.1MB/s]
 22%|██▏       | 483M/2.12G [00:09<00:52, 33.9MB/s]

 30%|██▉       | 519M/1.70G [00:09<00:20, 62.0MB/s]
 23%|██▎       | 491M/2.12G [00:10<00:41, 41.9MB/s]

 30%|███       | 527M/1.70G [00:09<00:19, 65.6MB/s]
 23%|██▎       | 497M/2.12G [00:10<00:47, 37.1MB/s]
 23%|██▎       | 506M/2.12G [00:10<00:36, 47.1MB/s]

 31%|███       | 535M/1.70G [00:10<00:26, 47.1MB/s]
 24%|██▎       | 513M/2.12G [00:10<00:35, 49.5MB/s]

 31%|███       | 541M/1.70G [00:10<00:25, 49.2MB/s]
 24%|██▍       | 523M/2.12G [00:10<00:28, 60.8MB/s]

 32%|███▏      | 551M/1.70G [00:10<00:20, 60.3MB/s]
 24%|██▍       | 530M/2.12G [00:10<00:30, 56.8MB/s]

 32%|███▏      | 561M/1.70G [00:10<00:18, 65.7MB/s]
 25%|██▍       | 537M/2.12G [00:10<00:29, 58.3MB/s]

 33%|███▎      | 569M/1.70G [00:10<00:18, 67.2MB/s]
 25%|██▌       | 547M/2.12G [00:10<00:24, 68.2MB/s]

 33%|███▎      | 578M/1.70G [00:10<00:16, 73.2MB/s]
 26%|██▌       | 557M/2.12G [00:11<00:21, 77.0MB/s]

 34%|███▎      | 586M/1.70G [00:10<00:17, 71.0MB/s]

 34%|███▍      | 595M/1.70G [00:11<00:15, 76.1MB/s]
 26%|██▌       | 565M/2.12G [00:11<00:24, 69.2MB/s]

 35%|███▍      | 609M/1.70G [00:11<00:13, 88.9MB/s]
 26%|██▋       | 573M/2.12G [00:11<00:26, 63.3MB/s]

 35%|███▌      | 618M/1.70G [00:11<00:13, 90.0MB/s]
 27%|██▋       | 583M/2.12G [00:11<00:23, 71.8MB/s]

 36%|███▌      | 630M/1.70G [00:11<00:11, 99.5MB/s]

 37%|███▋      | 640M/1.70G [00:12<00:37, 31.0MB/s]
 27%|██▋       | 591M/2.12G [00:12<01:09, 23.8MB/s]

 37%|███▋      | 650M/1.70G [00:12<00:29, 38.7MB/s]
 28%|██▊       | 602M/2.12G [00:12<00:49, 32.9MB/s]
 28%|██▊       | 611M/2.12G [00:12<00:40, 40.3MB/s]

 38%|███▊      | 660M/1.70G [00:12<00:24, 46.6MB/s]

 39%|███▊      | 673M/1.70G [00:12<00:18, 60.3MB/s]
 29%|██▊       | 619M/2.12G [00:12<00:37, 43.2MB/s]

 39%|███▉      | 684M/1.70G [00:12<00:15, 69.5MB/s]
 29%|██▉       | 626M/2.12G [00:12<00:35, 45.0MB/s]

 40%|████      | 697M/1.70G [00:12<00:13, 79.8MB/s]
 29%|██▉       | 638M/2.12G [00:13<00:27, 59.2MB/s]

 41%|████      | 707M/1.70G [00:12<00:12, 83.7MB/s]
 30%|██▉       | 646M/2.12G [00:13<00:25, 63.4MB/s]

 41%|████      | 717M/1.70G [00:13<00:12, 88.5MB/s]
 30%|███       | 654M/2.12G [00:13<00:23, 67.5MB/s]
 31%|███       | 662M/2.12G [00:13<00:22, 70.8MB/s]

 42%|████▏     | 727M/1.70G [00:13<00:15, 67.7MB/s]

 42%|████▏     | 736M/1.70G [00:13<00:15, 68.8MB/s]
 31%|███       | 670M/2.12G [00:13<00:26, 58.5MB/s]

 43%|████▎     | 744M/1.70G [00:13<00:15, 67.3MB/s]
 31%|███▏      | 677M/2.12G [00:13<00:29, 53.2MB/s]

 43%|████▎     | 755M/1.70G [00:13<00:13, 77.3MB/s]
 32%|███▏      | 683M/2.12G [00:13<00:28, 54.4MB/s]

 44%|████▍     | 765M/1.70G [00:13<00:12, 83.9MB/s]
 32%|███▏      | 690M/2.12G [00:13<00:26, 58.6MB/s]

 44%|████▍     | 774M/1.70G [00:13<00:13, 77.1MB/s]
 32%|███▏      | 703M/2.12G [00:14<00:19, 77.2MB/s]

 45%|████▌     | 786M/1.70G [00:14<00:11, 88.4MB/s]
 33%|███▎      | 712M/2.12G [00:14<00:20, 73.5MB/s]

 46%|████▌     | 797M/1.70G [00:14<00:10, 94.8MB/s]
 33%|███▎      | 722M/2.12G [00:14<00:19, 79.4MB/s]

 46%|████▋     | 807M/1.70G [00:14<00:10, 93.8MB/s]
 34%|███▍      | 731M/2.12G [00:14<00:18, 81.6MB/s]

 47%|████▋     | 817M/1.70G [00:14<00:10, 90.0MB/s]
 34%|███▍      | 740M/2.12G [00:14<00:17, 83.8MB/s]

 48%|████▊     | 829M/1.70G [00:14<00:09, 98.2MB/s]
 35%|███▍      | 749M/2.12G [00:14<00:17, 84.2MB/s]

 48%|████▊     | 839M/1.70G [00:14<00:09, 95.3MB/s]
 35%|███▌      | 759M/2.12G [00:14<00:16, 89.8MB/s]
 36%|███▌      | 769M/2.12G [00:14<00:15, 93.3MB/s]

 49%|████▉     | 849M/1.70G [00:14<00:09, 93.8MB/s]
 36%|███▌      | 780M/2.12G [00:14<00:14, 97.7MB/s]

 49%|████▉     | 859M/1.70G [00:14<00:10, 91.0MB/s]
 37%|███▋      | 793M/2.12G [00:15<00:13, 106MB/s] 

 50%|████▉     | 868M/1.70G [00:14<00:10, 89.9MB/s]
 37%|███▋      | 804M/2.12G [00:15<00:13, 107MB/s]

 50%|█████     | 877M/1.70G [00:15<00:10, 87.0MB/s]

 51%|█████     | 886M/1.70G [00:15<00:10, 85.0MB/s]
 38%|███▊      | 815M/2.12G [00:15<00:16, 84.8MB/s]

 51%|█████▏    | 895M/1.70G [00:15<00:12, 69.5MB/s]
 38%|███▊      | 824M/2.12G [00:15<00:18, 74.1MB/s]

 52%|█████▏    | 904M/1.70G [00:15<00:11, 73.7MB/s]
 38%|███▊      | 832M/2.12G [00:15<00:18, 75.1MB/s]

 52%|█████▏    | 912M/1.70G [00:15<00:11, 75.6MB/s]
 39%|███▉      | 843M/2.12G [00:15<00:16, 83.7MB/s]
 40%|███▉      | 856M/2.12G [00:15<00:14, 95.7MB/s]

 53%|█████▎    | 921M/1.70G [00:15<00:12, 71.1MB/s]
 40%|███▉      | 866M/2.12G [00:15<00:13, 97.5MB/s]

 53%|█████▎    | 931M/1.70G [00:15<00:10, 77.9MB/s]
 41%|████      | 878M/2.12G [00:16<00:12, 104MB/s] 

 54%|█████▍    | 939M/1.70G [00:16<00:11, 70.3MB/s]
 41%|████      | 889M/2.12G [00:16<00:12, 104MB/s]

 55%|█████▍    | 950M/1.70G [00:16<00:10, 80.9MB/s]

 56%|█████▌    | 967M/1.70G [00:16<00:07, 105MB/s] 
 42%|████▏     | 900M/2.12G [00:16<00:17, 73.9MB/s]

 56%|█████▌    | 978M/1.70G [00:16<00:07, 105MB/s]
 42%|████▏     | 909M/2.12G [00:16<00:17, 76.9MB/s]

 57%|█████▋    | 989M/1.70G [00:16<00:07, 103MB/s]
 43%|████▎     | 921M/2.12G [00:16<00:14, 87.9MB/s]

 57%|█████▋    | 0.98G/1.70G [00:16<00:07, 105MB/s]

 58%|█████▊    | 0.99G/1.70G [00:16<00:06, 110MB/s]
 43%|████▎     | 931M/2.12G [00:16<00:15, 81.4MB/s]
 43%|████▎     | 940M/2.12G [00:16<00:15, 82.6MB/s]

 59%|█████▊    | 1.00G/1.70G [00:16<00:09, 76.3MB/s]
 44%|████▍     | 949M/2.12G [00:17<00:18, 70.7MB/s]
 44%|████▍     | 957M/2.12G [00:17<00:18, 67.3MB/s]

 59%|█████▉    | 1.01G/1.70G [00:17<00:11, 64.5MB/s]
 45%|████▍     | 964M/2.12G [00:17<00:19, 65.9MB/s]

 60%|█████▉    | 1.02G/1.70G [00:17<00:11, 64.5MB/s]
 45%|████▍     | 971M/2.12G [00:17<00:19, 65.2MB/s]

 60%|██████    | 1.02G/1.70G [00:17<00:11, 63.6MB/s]
 45%|████▌     | 978M/2.12G [00:17<00:19, 63.6MB/s]

 61%|██████    | 1.03G/1.70G [00:17<00:11, 62.9MB/s]
 45%|████▌     | 985M/2.12G [00:17<00:19, 64.7MB/s]

 61%|██████    | 1.04G/1.70G [00:17<00:11, 62.8MB/s]
 46%|████▌     | 992M/2.12G [00:17<00:18, 65.3MB/s]

 61%|██████▏   | 1.04G/1.70G [00:17<00:10, 64.5MB/s]
 46%|████▌     | 0.98G/2.12G [00:17<00:17, 68.9MB/s]

 62%|██████▏   | 1.05G/1.70G [00:17<00:10, 66.5MB/s]
 46%|████▋     | 0.98G/2.12G [00:18<00:17, 69.8MB/s]

 62%|██████▏   | 1.06G/1.70G [00:17<00:10, 67.7MB/s]
 47%|████▋     | 0.99G/2.12G [00:18<00:16, 73.2MB/s]

 63%|██████▎   | 1.07G/1.70G [00:18<00:09, 69.0MB/s]
 47%|████▋     | 1.00G/2.12G [00:18<00:16, 72.2MB/s]

 63%|██████▎   | 1.07G/1.70G [00:18<00:09, 68.5MB/s]
 48%|████▊     | 1.01G/2.12G [00:18<00:16, 73.8MB/s]

 63%|██████▎   | 1.08G/1.70G [00:18<00:09, 69.1MB/s]
 48%|████▊     | 1.01G/2.12G [00:18<00:18, 63.5MB/s]

 64%|██████▍   | 1.09G/1.70G [00:18<00:10, 61.4MB/s]
 48%|████▊     | 1.02G/2.12G [00:18<00:20, 57.1MB/s]

 64%|██████▍   | 1.09G/1.70G [00:18<00:11, 55.9MB/s]
 49%|████▊     | 1.03G/2.12G [00:18<00:20, 57.9MB/s]

 65%|██████▍   | 1.10G/1.70G [00:18<00:11, 56.5MB/s]
 49%|████▉     | 1.04G/2.12G [00:18<00:18, 63.5MB/s]

 65%|██████▍   | 1.10G/1.70G [00:18<00:11, 57.4MB/s]
 49%|████▉     | 1.04G/2.12G [00:19<00:18, 63.4MB/s]

 65%|██████▌   | 1.11G/1.70G [00:18<00:10, 58.5MB/s]
 50%|████▉     | 1.05G/2.12G [00:19<00:17, 64.7MB/s]

 66%|██████▌   | 1.12G/1.70G [00:19<00:10, 61.8MB/s]
 50%|████▉     | 1.06G/2.12G [00:19<00:16, 67.6MB/s]

 66%|██████▌   | 1.12G/1.70G [00:19<00:09, 63.1MB/s]
 50%|█████     | 1.06G/2.12G [00:19<00:15, 70.6MB/s]

 67%|██████▋   | 1.13G/1.70G [00:19<00:08, 69.1MB/s]
 51%|█████     | 1.07G/2.12G [00:19<00:15, 72.6MB/s]

 67%|██████▋   | 1.14G/1.70G [00:19<00:08, 71.4MB/s]
 51%|█████     | 1.08G/2.12G [00:19<00:15, 73.6MB/s]

 68%|██████▊   | 1.15G/1.70G [00:19<00:07, 74.3MB/s]
 51%|█████▏    | 1.09G/2.12G [00:19<00:14, 76.0MB/s]

 68%|██████▊   | 1.16G/1.70G [00:19<00:07, 74.0MB/s]
 52%|█████▏    | 1.10G/2.12G [00:19<00:15, 69.1MB/s]

 69%|██████▊   | 1.17G/1.70G [00:19<00:08, 69.9MB/s]
 52%|█████▏    | 1.10G/2.12G [00:19<00:15, 70.8MB/s]

 69%|██████▉   | 1.17G/1.70G [00:19<00:08, 70.0MB/s]
 52%|█████▏    | 1.11G/2.12G [00:20<00:16, 66.4MB/s]

 69%|██████▉   | 1.18G/1.70G [00:19<00:09, 60.2MB/s]

 70%|██████▉   | 1.19G/1.70G [00:20<00:08, 62.5MB/s]
 53%|█████▎    | 1.12G/2.12G [00:20<00:19, 54.8MB/s]

 70%|███████   | 1.19G/1.70G [00:20<00:08, 63.8MB/s]
 53%|█████▎    | 1.12G/2.12G [00:20<00:18, 57.7MB/s]

 71%|███████   | 1.20G/1.70G [00:20<00:08, 64.8MB/s]
 53%|█████▎    | 1.13G/2.12G [00:20<00:17, 61.3MB/s]

 71%|███████   | 1.21G/1.70G [00:20<00:07, 68.3MB/s]
 54%|█████▍    | 1.14G/2.12G [00:20<00:16, 65.0MB/s]

 71%|███████▏  | 1.21G/1.70G [00:20<00:07, 66.1MB/s]
 54%|█████▍    | 1.15G/2.12G [00:20<00:16, 64.4MB/s]

 72%|███████▏  | 1.22G/1.70G [00:20<00:07, 66.4MB/s]
 54%|█████▍    | 1.15G/2.12G [00:20<00:16, 63.0MB/s]

 72%|███████▏  | 1.23G/1.70G [00:20<00:08, 61.2MB/s]
 55%|█████▍    | 1.16G/2.12G [00:20<00:16, 61.6MB/s]

 73%|███████▎  | 1.23G/1.70G [00:20<00:08, 58.0MB/s]
 55%|█████▌    | 1.17G/2.12G [00:21<00:16, 61.0MB/s]
 55%|█████▌    | 1.17G/2.12G [00:22<01:11, 14.1MB/s]

 73%|███████▎  | 1.24G/1.70G [00:22<00:37, 13.4MB/s]
 56%|█████▌    | 1.18G/2.12G [00:22<00:51, 19.6MB/s]

 73%|███████▎  | 1.25G/1.70G [00:22<00:23, 20.3MB/s]
 56%|█████▌    | 1.19G/2.12G [00:22<00:42, 23.4MB/s]

 74%|███████▍  | 1.25G/1.70G [00:22<00:19, 24.2MB/s]

 74%|███████▍  | 1.26G/1.70G [00:22<00:13, 34.0MB/s]

 75%|███████▌  | 1.28G/1.70G [00:22<00:08, 50.6MB/s]

 76%|███████▌  | 1.29G/1.70G [00:22<00:07, 59.8MB/s]
 57%|█████▋    | 1.20G/2.12G [00:23<00:35, 27.6MB/s]
 57%|█████▋    | 1.21G/2.12G [00:23<00:26, 36.3MB/s]

 76%|███████▋  | 1.30G/1.70G [00:23<00:07, 57.6MB/s]

 77%|███████▋  | 1.31G/1.70G [00:23<00:06, 62.4MB/s]
 57%|█████▋    | 1.21G/2.12G [00:23<00:27, 35.1MB/s]
 58%|█████▊    | 1.22G/2.12G [00:23<00:24, 38.9MB/s]

 77%|███████▋  | 1.31G/1.70G [00:23<00:07, 53.2MB/s]
 58%|█████▊    | 1.23G/2.12G [00:23<00:22, 43.0MB/s]

 78%|███████▊  | 1.32G/1.70G [00:23<00:06, 61.9MB/s]

 78%|███████▊  | 1.33G/1.70G [00:23<00:05, 68.7MB/s]
 58%|█████▊    | 1.24G/2.12G [00:23<00:20, 46.8MB/s]

 79%|███████▉  | 1.34G/1.70G [00:23<00:05, 72.2MB/s]
 59%|█████▉    | 1.24G/2.12G [00:23<00:20, 46.0MB/s]

 80%|███████▉  | 1.35G/1.70G [00:23<00:04, 77.2MB/s]

 80%|███████▉  | 1.36G/1.70G [00:23<00:04, 74.6MB/s]
 59%|█████▉    | 1.25G/2.12G [00:24<00:19, 47.6MB/s]

 81%|████████  | 1.37G/1.70G [00:24<00:04, 79.9MB/s]
 60%|█████▉    | 1.26G/2.12G [00:24<00:16, 56.2MB/s]

 81%|████████  | 1.38G/1.70G [00:24<00:03, 88.6MB/s]
 60%|█████▉    | 1.27G/2.12G [00:24<00:18, 48.6MB/s]

 82%|████████▏ | 1.39G/1.70G [00:24<00:03, 87.9MB/s]
 60%|██████    | 1.27G/2.12G [00:24<00:16, 55.1MB/s]

 82%|████████▏ | 1.40G/1.70G [00:24<00:03, 81.4MB/s]
 61%|██████    | 1.28G/2.12G [00:24<00:14, 62.4MB/s]

 83%|████████▎ | 1.41G/1.70G [00:24<00:03, 82.2MB/s]
 61%|██████    | 1.29G/2.12G [00:24<00:14, 62.8MB/s]

 83%|████████▎ | 1.42G/1.70G [00:24<00:03, 85.9MB/s]
 61%|██████▏   | 1.30G/2.12G [00:24<00:13, 64.0MB/s]

 84%|████████▍ | 1.43G/1.70G [00:24<00:02, 99.6MB/s]

 85%|████████▍ | 1.45G/1.70G [00:24<00:02, 109MB/s] 
 62%|██████▏   | 1.31G/2.12G [00:25<00:15, 57.2MB/s]

 86%|████████▌ | 1.46G/1.70G [00:25<00:02, 110MB/s]
 62%|██████▏   | 1.32G/2.12G [00:25<00:12, 69.9MB/s]

 86%|████████▌ | 1.47G/1.70G [00:25<00:02, 110MB/s]
 63%|██████▎   | 1.33G/2.12G [00:25<00:11, 74.1MB/s]

 87%|████████▋ | 1.48G/1.70G [00:25<00:02, 102MB/s]
 63%|██████▎   | 1.34G/2.12G [00:25<00:10, 82.6MB/s]

 87%|████████▋ | 1.49G/1.70G [00:25<00:02, 96.1MB/s]
 64%|██████▎   | 1.34G/2.12G [00:25<00:09, 85.4MB/s]

 88%|████████▊ | 1.50G/1.70G [00:25<00:02, 106MB/s] 
 64%|██████▍   | 1.35G/2.12G [00:25<00:11, 71.7MB/s]

 89%|████████▉ | 1.52G/1.70G [00:25<00:01, 119MB/s]
 65%|██████▍   | 1.37G/2.12G [00:25<00:09, 84.5MB/s]

 90%|████████▉ | 1.53G/1.70G [00:25<00:01, 117MB/s]
 65%|██████▍   | 1.37G/2.12G [00:25<00:11, 69.7MB/s]

 91%|█████████ | 1.54G/1.70G [00:25<00:01, 99.4MB/s]
 65%|██████▌   | 1.38G/2.12G [00:26<00:10, 74.7MB/s]

 91%|█████████ | 1.55G/1.70G [00:25<00:01, 99.8MB/s]
 66%|██████▌   | 1.39G/2.12G [00:26<00:10, 74.7MB/s]

 92%|█████████▏| 1.56G/1.70G [00:26<00:01, 102MB/s] 
 66%|██████▋   | 1.40G/2.12G [00:26<00:09, 83.9MB/s]

 93%|█████████▎| 1.57G/1.70G [00:26<00:01, 108MB/s]
 67%|██████▋   | 1.41G/2.12G [00:26<00:08, 91.4MB/s]

 93%|█████████▎| 1.58G/1.70G [00:26<00:01, 100MB/s]
 67%|██████▋   | 1.42G/2.12G [00:26<00:08, 90.9MB/s]

 94%|█████████▎| 1.59G/1.70G [00:26<00:01, 87.1MB/s]
 68%|██████▊   | 1.43G/2.12G [00:26<00:08, 86.6MB/s]

 94%|█████████▍| 1.60G/1.70G [00:26<00:01, 82.8MB/s]
 68%|██████▊   | 1.44G/2.12G [00:26<00:10, 70.8MB/s]

 95%|█████████▍| 1.61G/1.70G [00:26<00:01, 78.2MB/s]
 68%|██████▊   | 1.45G/2.12G [00:26<00:09, 73.6MB/s]

 95%|█████████▌| 1.62G/1.70G [00:26<00:01, 83.3MB/s]
 69%|██████▉   | 1.46G/2.12G [00:27<00:08, 80.8MB/s]

 96%|█████████▌| 1.63G/1.70G [00:26<00:00, 85.3MB/s]
 69%|██████▉   | 1.47G/2.12G [00:27<00:08, 81.4MB/s]

 96%|█████████▋| 1.64G/1.70G [00:27<00:00, 87.5MB/s]
 70%|██████▉   | 1.48G/2.12G [00:27<00:07, 86.9MB/s]

 97%|█████████▋| 1.65G/1.70G [00:27<00:00, 70.2MB/s]
 70%|███████   | 1.48G/2.12G [00:27<00:10, 67.4MB/s]

 97%|█████████▋| 1.66G/1.70G [00:27<00:00, 75.5MB/s]
 71%|███████   | 1.49G/2.12G [00:27<00:09, 71.1MB/s]

 98%|█████████▊| 1.67G/1.70G [00:27<00:00, 82.3MB/s]
 71%|███████   | 1.50G/2.12G [00:27<00:08, 74.9MB/s]

 99%|█████████▊| 1.68G/1.70G [00:27<00:00, 90.1MB/s]
 71%|███████▏  | 1.51G/2.12G [00:27<00:08, 75.7MB/s]

 99%|█████████▉| 1.69G/1.70G [00:27<00:00, 89.7MB/s]
 72%|███████▏  | 1.52G/2.12G [00:27<00:08, 79.7MB/s]

100%|██████████| 1.70G/1.70G [00:27<00:00, 65.5MB/s]

 72%|███████▏  | 1.53G/2.12G [00:28<00:08, 76.6MB/s]
 73%|███████▎  | 1.54G/2.12G [00:28<00:06, 98.0MB/s]
 74%|███████▎  | 1.56G/2.12G [00:28<00:05, 112MB/s] 
 74%|███████▍  | 1.57G/2.12G [00:28<00:05, 105MB/s]
 75%|███████▍  | 1.58G/2.12G [00:28<00:05, 107MB/s]
 75%|███████▌  | 1.59G/2.12G [00:28<00:06, 89.3MB/s]
 76%|███████▌  | 1.61G/2.12G [00:28<00:05, 101MB/s] 
 77%|███████▋  | 1.62G/2.12G [00:28<00:04, 112MB/s]
 77%|███████▋  | 1.63G/2.12G [00:29<00:05, 92.3MB/s]
 78%|███████▊  | 1.64G/2.12G [00:29<00:06, 78.4MB/s]
 78%|███████▊  | 1.66G/2.12G [00:29<00:05, 95.5MB/s]
 79%|███████▉  | 1.67G/2.12G [00:29<00:04, 101MB/s] 
 80%|███████▉  | 1.69G/2.12G [00:29<00:04, 114MB/s]
 80%|████████  | 1.70G/2.12G [00:29<00:03, 124MB/s]
 81%|████████  | 1.71G/2.12G [00:29<00:03, 122MB/s]
 82%|████████▏ | 1.73G/2.12G [00:30<00:04, 95.5MB/s]
 82%|████████▏ | 1.74G/2.12G [00:30<00:03, 104MB/s] 
 83%|████████▎ | 1.75G/2.12G [00:30<00:03, 103MB/s]
 83%|████████▎ | 1.76G/2.12G [00:30<00:04, 76.8MB/s]
 84%|████████▍ | 1.78G/2.12G [00:30<00:03, 96.8MB/s]
 85%|████████▍ | 1.79G/2.12G [00:30<00:03, 102MB/s] 
 85%|████████▌ | 1.80G/2.12G [00:31<00:04, 69.5MB/s]
 86%|████████▌ | 1.82G/2.12G [00:31<00:03, 92.2MB/s]
 87%|████████▋ | 1.83G/2.12G [00:31<00:03, 91.5MB/s]
 87%|████████▋ | 1.84G/2.12G [00:31<00:02, 98.1MB/s]
 88%|████████▊ | 1.85G/2.12G [00:31<00:03, 85.1MB/s]
 88%|████████▊ | 1.86G/2.12G [00:31<00:03, 88.0MB/s]
 89%|████████▊ | 1.87G/2.12G [00:31<00:02, 88.9MB/s]
 89%|████████▉ | 1.88G/2.12G [00:32<00:02, 93.5MB/s]
 90%|████████▉ | 1.90G/2.12G [00:32<00:02, 106MB/s] 
 90%|█████████ | 1.91G/2.12G [00:32<00:01, 111MB/s]
 91%|█████████ | 1.92G/2.12G [00:32<00:02, 98.7MB/s]
 91%|█████████▏| 1.93G/2.12G [00:32<00:01, 107MB/s] 
 92%|█████████▏| 1.95G/2.12G [00:32<00:01, 104MB/s]
 93%|█████████▎| 1.96G/2.12G [00:32<00:01, 117MB/s]
 93%|█████████▎| 1.97G/2.12G [00:32<00:01, 106MB/s]
 94%|█████████▍| 1.98G/2.12G [00:33<00:01, 92.6MB/s]
 94%|█████████▍| 1.99G/2.12G [00:33<00:01, 86.4MB/s]
 95%|█████████▍| 2.00G/2.12G [00:33<00:01, 68.3MB/s]
 95%|█████████▌| 2.02G/2.12G [00:33<00:01, 84.0MB/s]
 96%|█████████▌| 2.03G/2.12G [00:33<00:01, 91.6MB/s]
 96%|█████████▋| 2.04G/2.12G [00:33<00:00, 96.2MB/s]
 97%|█████████▋| 2.05G/2.12G [00:33<00:00, 108MB/s] 
 98%|█████████▊| 2.06G/2.12G [00:33<00:00, 89.9MB/s]
 98%|█████████▊| 2.08G/2.12G [00:34<00:00, 103MB/s] 
 99%|█████████▉| 2.09G/2.12G [00:34<00:00, 115MB/s]
100%|██████████| 2.12G/2.12G [00:34<00:00, 66.0MB/s]

print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1

Controlla la posizione dei pesi del modello e del tokenizzatore, quindi imposta le variabili di percorso. La directory del tokenizzatore si troverà nella directory principale in cui hai scaricato il modello, mentre i pesi del modello saranno in una sottodirectory. Ad esempio:

Il file tokenizer.model sarà in /LOCAL/PATH/TO/gemma/flax/2b-it/2).
il checkpoint del modello sarà in /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it).

CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)
TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')
print('CKPT_PATH:', CKPT_PATH)
print('TOKENIZER_PATH:', TOKENIZER_PATH)

CKPT_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/gemma2-2b-it
TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/tokenizer.model

Eseguire campionamento/inferenza

Carica e formatta il checkpoint del modello Gemma con il metodo gemma.params.load_and_format_params:

from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)

Carica il tokenizzatore Gemma, creato con sentencepiece.SentencePieceProcessor:

import sentencepiece as spm

vocab = spm.SentencePieceProcessor()
vocab.Load(TOKENIZER_PATH)

True

Per caricare automaticamente la configurazione corretta dal checkpoint del modello Gemma, utilizza gemma.transformer.TransformerConfig. L'argomento cache_size è il numero di passi temporali nella cache Transformer di Gemma. In seguito, crea un'istanza del modello Gemma come transformer con gemma.transformer.Transformer (che eredita da flax.linen.Module).

di Gemini Advanced.

from gemma import transformer as transformer_lib

transformer_config = transformer_lib.TransformerConfig.from_params(
    params=params,
    cache_size=1024
)

transformer = transformer_lib.Transformer(transformer_config)

Crea un sampler con gemma.sampler.Sampler sopra il checkpoint/le ponderazioni del modello Gemma e il tokenizzatore:

from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)

Scrivi un prompt in input_batch ed esegui l'inferenza. Puoi modificare total_generation_steps (il numero di passaggi eseguiti durante la generazione di una risposta; questo esempio utilizza 100 per preservare la memoria dell'host).

di Gemini Advanced.

prompt = [
    "what is JAX in 3 bullet points?",
]

reply = sampler(input_strings=prompt,
                total_generation_steps=128,
                )

for input_string, out_string in zip(prompt, reply.text):
    print(f"Prompt:\n{input_string}\nOutput:\n{out_string}")

Prompt:
what is JAX in 3 bullet points?
Output:


* **High-performance numerical computation:** JAX leverages the power of GPUs and TPUs to accelerate complex mathematical operations, making it ideal for scientific computing, machine learning, and data analysis.
* **Automatic differentiation:** JAX provides automatic differentiation capabilities, allowing you to compute gradients and optimize models efficiently. This simplifies the process of training deep learning models.
* **Functional programming:** JAX embraces functional programming principles, promoting code readability and maintainability. It offers a flexible and expressive syntax for defining and manipulating data. 


<end_of_turn>

(Facoltativo) Esegui questa cella per liberare memoria se hai completato il blocco note e vuoi provare un altro prompt. In seguito, puoi creare di nuovo un'istanza per sampler nel passaggio 3 e personalizzare ed eseguire la richiesta nel passaggio 4.

del sampler

Scopri di più

Puoi scoprire di più sulla libreria gemma di Google DeepMind su GitHub, che contiene le stringhe di documenti dei moduli che hai utilizzato in questo tutorial, ad esempio gemma.params, gemma.transformer e gemma.sampler.
Le seguenti librerie dispongono di siti di documentazione proprietari: JAX di base, Flax e Orbax.
Per la documentazione relativa al tokenizzatore/detokenizzatore sentencepiece, consulta il repository GitHub sentencepiece di Google.
Per la documentazione relativa a kagglehub, dai un'occhiata a README.md nel repository GitHub kagglehub di Kaggle.
Scopri come utilizzare i modelli Gemma con Vertex AI di Google Cloud.

Inferenza con Gemma utilizzando JAX e Lino

Panoramica

Configurazione

1. Configura l'accesso a Kaggle per Gemma

2. Imposta le variabili di ambiente

3. Installa la libreria gemma

Carica e prepara il modello Gemma

Eseguire campionamento/inferenza

Scopri di più

3. Installa la libreria `gemma`